Number Plate Recognition

Automatic Number Plate Recognition technology can be a tool applied to smart cities in investigation and crime prevention. Despite the massive number of both commercial and academic methods for Automatic Number plate Recognition (ANPR), most existing approaches are focused on a selected number plate (NP) region (e.g. European, US, Brazilian, Taiwanese.) and regularly explore datasets containing approximately frontal images. It has been widely utilized in parking management systems and toll booths on highways which have a rigid shooting angle and lighting surroundings. If the vehicle is authenticated then it gets easy to find information about it. But if the vehicle is an un-authenticate, then it becomes a very tedious and time-consuming task and very hard to search that vehicle. Recognized number plate displays on graphical user interface and stored into a database with time and date for further use. It will be beneficial to scale back the matter like traffic violation cases and to reinforce security in parking areas. Computer vision technology plays a really pivotal role during this project for moving vehicle number plate character recognition. Images from video sequences are taken to acknowledge the plate characters. Character recognition technique from the licensed number plates supported a fore mentioned system recognizes and differentiates between genuine and faux number plates. In the project that we present number plate characters are easily identifiable from the machine learning algorithms incorporated in our system.